Refactor contest data handling in app.py to improve player data processing

- Updated session state references from 'Adj_Contest' to 'Contest' for consistency and clarity.
- Simplified mapping dictionary creation by directly using existing session state dictionaries, enhancing code readability.
- Removed unused logic related to 'Showdown' type processing, streamlining the data handling process.
- These changes contribute to ongoing efforts to enhance data integrity and improve user experience within the application.

app.py

@@ -107,25 +107,21 @@ with tab1:
 
 with tab2:
     excluded_cols = ['BaseName', 'EntryCount']
-    if 'Adj_Contest' in st.session_state and 'Adj_projections_df' in st.session_state:
-        player_columns = [col for col in st.session_state['Adj_Contest'].columns if col not in excluded_cols]
+    if 'Contest' in st.session_state:
+        player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
         for col in player_columns:
-            st.session_state['Adj_Contest'][col] = st.session_state['Adj_Contest'][col].astype(str)
+            st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str)
     
         # Create mapping dictionaries
         map_dict = {
-            'pos_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['position'])),
-            'team_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['team'])),
-            'salary_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['salary'])),
-            'proj_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['median'])),
-            'own_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['ownership'])),
-            'own_percent_rank': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['ownership'].rank(pct=True))),
-            'cpt_salary_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['salary'])),
-            'cpt_proj_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['median'] * 1.5)),
-            'cpt_own_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['captain ownership']))
+            'pos_map': st.session_state['pos_dict'],
+            'team_map': st.session_state['team_dict'],
+            'salary_map': st.session_state['salary_dict'],
+            'own_map': st.session_state['ownership_dict'],
+            'own_percent_rank': dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own'].rank(pct=True)))
         }
         # Create a copy of the dataframe for calculations
-        working_df = st.session_state['Adj_Contest'].copy() 
+        working_df = st.session_state['Contest'].copy() 
 
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
@@ -143,9 +139,7 @@ with tab2:
                 axis=1
             )
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-            working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
             working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
-            working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
             working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
@@ -156,45 +150,6 @@ with tab2:
             working_df['percentile_finish'] = working_df['index'].rank(pct=True)
             working_df['finish'] = working_df['index']
             working_df = working_df.drop(['sorted', 'index'], axis=1)
-        elif type_var == 'Showdown':
-            working_df['stack'] = working_df.apply(
-                lambda row: Counter(
-                    map_dict['team_map'].get(player, '') for player in row
-                    if map_dict['team_map'].get(player, '') != ''
-                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
-                axis=1
-            )
-            working_df['stack_size'] = working_df.apply(
-                lambda row: Counter(
-                    map_dict['team_map'].get(player, '') for player in row
-                    if map_dict['team_map'].get(player, '') != ''
-                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
-                axis=1
-            )
-            working_df['salary'] = working_df.apply(
-                lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
-                            sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
-            working_df['median'] = working_df.apply(
-                lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
-                            sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
-            working_df['Own'] = working_df.apply(
-                lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
-                            sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
-            working_df['sorted'] = working_df[player_columns].apply(
-                lambda row: row[0] + '|' + ','.join(sorted(row[1:].values)),
-                axis=1
-            )
-            working_df['dupes'] = working_df.groupby('sorted').transform('size')
-            working_df = working_df.reset_index()
-            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
-            working_df['finish'] = working_df['index']
-            working_df = working_df.drop(['sorted', 'index'], axis=1)
         st.session_state['field_player_frame'] = create_player_exposures(working_df, player_columns)
         st.session_state['field_stack_frame'] = create_stack_exposures(working_df)